A Bayesian network model for estimating stoichiometric ratios of lake seston components

The elemental composition of seston provides insights into how lake food webs function and how nutrients cycle through the environment. Here, we describe a Bayesian network model that simultaneously estimates relationships between dissolved and particulate nutrients, suspended volatile and nonvolatile sediments, and algal chlorophyll. The model provides direct estimates of the phosphorus (P) and nitrogen (N) content of phytoplankton, suspended non-living organic matter, and suspended inorganic sediment. We applied this model to data collected from reservoirs in Missouri, USA, to test the validity of our assumed relationships. The results indicate that, on average among all samples, the ratio of N and P (N:P) in phytoplankton and non-living organic matter in these reservoirs were similar, although under nutrient replete conditions, N:P in phytoplankton decreased. P content of inorganic sediment was lower than in phytoplankton and non-living organic matter. The analysis also provided a means of tracking changes in the composition of whole seston over time. In addition to informing questions regarding seston stoichiometry, this modeling approach may inform efforts to manage lake eutrophication because it can improve traditional models of relationships between nutrients and chlorophyll in lakes.